Automated Ground Control Point Center Detection
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Solution Overview
Problem
Detecting ground control points in aerial images obtained by UAVs is a time-consuming and challenging manual process due to variations in image acquisition conditions, such as altitude, GPS signal variations, and changing weather conditions, which affect the size, luminosity, and contrast of the ground control points.
Innovation Solution
The system automates the detection of ground control points by receiving aerial images from a UAV, segmenting the images to identify groups related to each ground control point, and using a trained image classifier to determine the pixel coordinates of the center of each ground control point, thereby improving photogrammetric processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection of ground control points is performed, then accuracy of identification can be maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical detection with an automated image processing system that uses computer vision algorithms to identify ground control points in aerial images. The system automatically detects targets by analyzing image features, patterns, and geo-spatial data, eliminating the need for manual inspection while maintaining identification accuracy.
Solution Approach 2:
The system enables the aerial survey process to self-correct and self-identify ground control points through automated algorithms. The image processing system independently analyzes captured images, detects target patterns, and generates geo-spatial coordinates without requiring external manual intervention, making the process self-sufficient and time-efficient.
2Productivity
If automated image processing is implemented, then processing speed improves, but accuracy may deteriorate due to varying image conditions
Solution Approach 1:
The patent dynamically adjusts processing parameters based on image characteristics such as altitude, lighting conditions, and weather variations. The system modifies detection thresholds, contrast enhancement levels, and pattern recognition sensitivity according to the specific image conditions, enabling accurate target identification across diverse environmental scenarios while maintaining high processing speed.
Solution Approach 2:
The image processing system employs dynamic algorithms that adapt to varying image qualities in real-time. The detection process adjusts its parameters and complexity based on the specific characteristics of each image, allowing the system to maintain high accuracy whether processing clear high-contrast images or challenging low-visibility conditions, thereby resolving the trade-off between speed and precision.
3Reliability
If multiple aerial images are captured to ensure coverage, then complete detection is improved, but data processing complexity increases
Solution Approach 1:
The patent divides the large set of aerial images into smaller groups or batches for processing. The system segments the image dataset based on geo-spatial coverage, temporal sequence, or visual characteristics, allowing parallel processing of multiple image groups. This segmentation reduces the computational complexity of handling all images simultaneously while ensuring complete coverage through systematic processing of each segment.
Solution Approach 2:
The image processing system is designed with universal algorithms that can handle various image types, qualities, and conditions using a single integrated framework. The system performs multiple functions including detection, validation, coordinate extraction, and error correction within one processing pipeline, reducing overall system complexity despite processing multiple diverse aerial images for complete ground control point detection.
Data Source
AI summary
Methods, systems and apparatus, including computer programs encoded on computer storage media for determining a center location of a ground control point used in aerial surveys. Machine learning models are used to identify in digital images pixel coordinates of the ground control point identified in the digital images. These image pixel coordinates are used in photogrammetric processing and software.